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Thompson Sampling

Thompson sampling, named after William R. Thompson, is a heuristic for choosing actions that addresses the exploration-exploitation dilemma in the multi-armed bandit problem. It consists of choosing the action that maximizes the expected reward with respect to a randomly drawn belief.

Papers

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Showing 371–380 of 655 papers

TitleStatusHype
Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret Bounds—0
Posterior sampling for reinforcement learning: worst-case regret bounds—0
Posterior Sampling via Autoregressive Generation—0
Practical Adversarial Attacks on Stochastic Bandits via Fake Data Injection—0
Preferential Multi-Objective Bayesian Optimization—0
Prior-free and prior-dependent regret bounds for Thompson Sampling—0
Probabilistic Inference in Reinforcement Learning Done Right—0
Profitable Bandits—0
QoS-Aware Multi-Armed Bandits—0
Racing Thompson: an Efficient Algorithm for Thompson Sampling with Non-conjugate Priors—0
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